In [75]:
import numpy as np
import pandas as pd

import yfinance as yf #only for data in a daily basis, not work for financial data any more(under decryption by Github:https://github.com/ranaroussi/yfinance/issues/1407)
from yahooquery import Ticker #replacement for yf
import yahoo_fin.stock_info as si
from pandas_datareader import data as pdr


import scipy.stats
import seaborn as sns
import math

import matplotlib.pyplot as plt 
%matplotlib inline
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.offline as pyo
pyo.init_notebook_mode() #show pictures

import networkx as nx #neural network plot
In [ ]:
#data import -- si.tickers, yf.download, Ticker() #depend on its efficiency 

#data manipulation -- simple sieve， 

#data visulization -- sns.heatmap, Neural Network, Candlestick,
In [2]:
#part 1: data manipulation
In [3]:
#choose stock market
#si.tickers_dow();
#si.tickers_sp500();
#lets try nasdaq stocks
ticker_list = si.tickers_nasdaq();         
ticker_str = " ".join(ticker_list);
In [4]:
#download muti stocks' data
Multi_Stock = yf.download(tickers=ticker_str,period='5y',interval='1d',rounding=True);

[*********************100%***********************]  5346 of 5346 completed

64 Failed downloads:
- ARIZR: Period '5y' is invalid, must be one of ['1d', '5d']
- HSPOR: Period '5y' is invalid, must be one of ['1d', '5d']
- FXCOR: Period '5y' is invalid, must be one of ['1d', '5d']
- MCAFR: Period '5y' is invalid, must be one of ['1d', '5d']
- BSGAR: Period '5y' is invalid, must be one of ['1d', '5d']
- KYCHR: Period '5y' is invalid, must be one of ['1d', '5d']
- ACAXR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZXZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- ALSAR: Period '5y' is invalid, must be one of ['1d', '5d']
- NWFL: No data found for this date range, symbol may be delisted
- IMAQR: No data found for this date range, symbol may be delisted
- WTMAR: Period '5y' is invalid, must be one of ['1d', '5d']
- CSLMR: Period '5y' is invalid, must be one of ['1d', '5d']
- QOMOR: Period '5y' is invalid, must be one of ['1d', '5d']
- MCACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ATMVR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZAZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- HMACR: Period '5y' is invalid, must be one of ['1d', '5d']
- AIBBR: Period '5y' is invalid, must be one of ['1d', '5d']
- MARXR: Period '5y' is invalid, must be one of ['1d', '5d']
- BMAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- ATAKR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZXYZ.A: No data found, symbol may be delisted
- WINVR: Period '5y' is invalid, must be one of ['1d', '5d']
- GLLIR: Period '5y' is invalid, must be one of ['1d', '5d']
- GDSTR: Period '5y' is invalid, must be one of ['1d', '5d']
- PNACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZCZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- CLOER: Period '5y' is invalid, must be one of ['1d', '5d']
- KACLR: Period '5y' is invalid, must be one of ['1d', '5d']
- NVACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ADOCR: Period '5y' is invalid, must be one of ['1d', '5d']
- YOTAR: Period '5y' is invalid, must be one of ['1d', '5d']
- BREZR: Period '5y' is invalid, must be one of ['1d', '5d']
- EFHTR: Period '5y' is invalid, must be one of ['1d', '5d']
- BRACR: Period '5y' is invalid, must be one of ['1d', '5d']
- BWAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- SVIIR: Period '5y' is invalid, must be one of ['1d', '5d']
- MCAGR: Period '5y' is invalid, must be one of ['1d', '5d']
- GBBKR: Period '5y' is invalid, must be one of ['1d', '5d']
- RWODR: Period '5y' is invalid, must be one of ['1d', '5d']
- FLFVR: Period '5y' is invalid, must be one of ['1d', '5d']
- PPHPR: Period '5y' is invalid, must be one of ['1d', '5d']
- BLEUR: Period '5y' is invalid, must be one of ['1d', '5d']
- ATMCR: Period '5y' is invalid, must be one of ['1d', '5d']
- LBBBR: Period '5y' is invalid, must be one of ['1d', '5d']
- PLTNR: Period '5y' is invalid, must be one of ['1d', '5d']
- SAGAR: Period '5y' is invalid, must be one of ['1d', '5d']
- WHLRL: Period '5y' is invalid, must be one of ['1d', '5d']
- DMAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZJZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- ZVZZC: Period '5y' is invalid, must be one of ['1d', '5d']
- JWACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZVZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- FEXDR: Period '5y' is invalid, must be one of ['1d', '5d']
- EMCGR: Period '5y' is invalid, must be one of ['1d', '5d']
- BNIXR: Period '5y' is invalid, must be one of ['1d', '5d']
- GBRGR: Period '5y' is invalid, must be one of ['1d', '5d']
- BRLIR: Period '5y' is invalid, must be one of ['1d', '5d']
- ENERR: Period '5y' is invalid, must be one of ['1d', '5d']
- TENKR: Period '5y' is invalid, must be one of ['1d', '5d']
- STRCW: Period '5y' is invalid, must be one of ['1d', '5d']
- HHGCR: Period '5y' is invalid, must be one of ['1d', '5d']
- GFGDR: Period '5y' is invalid, must be one of ['1d', '5d']
In [5]:
#df is Daily_highest_return_dataframe
df = Multi_Stock['High']-Multi_Stock['Low'];
In [6]:
#simple sieve
#get rid of unnecessary stocks(fluctuation is too small) by value x with at least n days
#return adjusted df and a list of unnecessary stocks
x=1; n=60;
list_first = df.columns.values.tolist();
df = df.loc[:,(df > x).sum() >= n];

list_changed = df.columns.values.tolist();
list_drop = list(set(list_first) - set(list_changed));
In [7]:
df
Out[7]:
AADI AADR AAL AAOI AAON AAPL AAXJ ABCB ABCL ABEO ... ZIONP ZLAB ZM ZNTL ZS ZUMZ ZVRA ZYME ZYNE ZYXI
Date
2018-03-23 1.50 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2018-03-26 1.50 1.09 1.40 2.330 0.90 1.67 1.50 1.25 NaN 23.75 ... 0.00 1.83 NaN NaN 3.64 1.10 7.20 1.26 0.790 0.20
2018-03-27 1.50 1.54 1.80 1.089 0.70 2.06 1.66 1.80 NaN 36.25 ... 0.48 0.69 NaN NaN 2.00 1.30 6.38 0.64 0.420 0.44
2018-03-28 0.00 0.96 1.21 1.020 0.75 1.21 0.88 1.17 NaN 29.00 ... 0.33 0.43 NaN NaN 1.19 0.95 7.74 1.01 0.400 0.01
2018-03-29 6.00 1.31 1.40 1.310 1.15 1.22 1.22 0.85 NaN 31.25 ... 0.44 1.09 NaN NaN 1.65 0.90 11.20 0.66 0.390 0.26
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-03-16 0.64 0.79 0.60 0.105 5.81 4.82 1.21 3.95 0.30 0.16 ... 1.46 3.94 2.33 1.68 3.26 1.05 0.24 0.59 0.046 0.30
2023-03-17 0.63 0.15 0.50 0.250 2.18 2.46 0.59 2.19 0.27 0.20 ... 2.45 3.36 2.03 0.88 4.04 1.21 0.31 0.29 0.030 0.47
2023-03-20 0.21 0.24 0.34 0.104 1.58 3.67 0.64 1.92 0.32 0.19 ... 1.56 3.37 1.93 0.77 3.24 1.24 0.30 0.59 0.020 0.43
2023-03-21 0.27 0.04 0.29 0.120 1.94 2.86 0.53 1.04 0.25 0.21 ... 1.55 2.47 2.71 0.76 8.24 0.98 0.54 0.46 0.060 0.81
2023-03-22 0.52 0.71 0.64 0.123 2.27 4.33 1.02 1.97 0.40 0.22 ... 1.91 1.80 3.47 1.10 4.96 0.82 0.32 0.56 0.091 0.43

1258 rows × 2427 columns

In [8]:
len(list_drop)
Out[8]:
2919
In [9]:
#correlation matrix(upper tirangle)
corr_matrix = df.corr(method='pearson')
corr_df=pd.DataFrame(np.triu(corr_matrix, k=1))
corr_df.columns = df.columns.values.tolist()
corr_df.index = df.columns.values.tolist()
corr_df
Out[9]:
AADI AADR AAL AAOI AAON AAPL AAXJ ABCB ABCL ABEO ... ZIONP ZLAB ZM ZNTL ZS ZUMZ ZVRA ZYME ZYNE ZYXI
AADI 0.0 0.098523 0.211833 0.333831 -0.177675 -0.200345 -0.001942 -0.000432 0.209040 0.288911 ... -0.065743 -0.046352 0.362035 0.178082 -0.131746 -0.016986 0.353405 -0.066598 0.091470 -0.158302
AADR 0.0 0.000000 0.349612 0.215544 0.239279 0.193011 0.517819 0.300941 0.094999 0.145218 ... 0.196129 0.083399 0.229643 0.155949 0.134950 0.249821 0.137826 0.165893 0.016158 0.110042
AAL 0.0 0.000000 0.000000 0.289093 0.129335 -0.070105 0.351778 0.209619 0.235130 0.324993 ... 0.199030 -0.050850 0.133282 0.225169 -0.067166 0.143564 0.305853 0.131763 0.186724 0.060189
AAOI 0.0 0.000000 0.000000 0.000000 -0.187637 -0.343685 -0.006482 -0.065575 0.425338 0.590262 ... -0.016972 -0.256456 0.241906 0.095945 -0.327191 -0.139149 0.573468 0.020137 0.265796 -0.130573
AAON 0.0 0.000000 0.000000 0.000000 0.000000 0.383395 0.455248 0.324938 0.006834 -0.237876 ... 0.332896 0.213159 0.110968 -0.035399 0.235890 0.335116 -0.283772 0.170631 -0.151756 0.270748
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
ZUMZ 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.225569 0.236523 -0.141211 0.200960
ZVRA 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.116302 0.310526 -0.303607
ZYME 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.053725 0.364572
ZYNE 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.054540
ZYXI 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2427 rows × 2427 columns

In [10]:
#part 2: visualization
#just for fun
In [11]:
#sns.heatmap
#matrix of first 10 stocks
new_matrix = corr_df.iloc[:10,:10]
plt.figure(figsize=(10, 8))
sns.heatmap(new_matrix, annot=True,cmap='cividis')
plt.figure()
Out[11]:
<Figure size 640x480 with 0 Axes>
<Figure size 640x480 with 0 Axes>
In [12]:
#matrix
New_corr_df = df.corr(method='pearson')
In [13]:
first_50 = corr_df.iloc[:50,:50];
In [14]:
stocks = first_50.index.values;
New_matrix = np.asmatrix(corr_df)
New_matrix[0:50,0:50];
In [15]:
G = nx.from_numpy_matrix(New_matrix[0:50,0:50]);
G = nx.relabel_nodes(G,lambda x: stocks[x]);
G.edges(data=True);
In [16]:
#neural network plot of first 50-stocks' correlation(original) 
def create_corr_network_1(G, title:str):
    #crates a list for edges and for the weights
    edges,weights = zip(*nx.get_edge_attributes(G,'weight').items())

    #positions
    positions=nx.circular_layout(G)
    
    #Figure size
    plt.figure(figsize=(10,10))

    #draws nodes
    nx.draw_networkx_nodes(G,positions,node_color='#DA70D6',
                           node_size=400,alpha=0.8)
    
    #Styling for labels
    nx.draw_networkx_labels(G, positions, font_size=7, 
                            font_family='sans-serif')
        
    #draws the edges
    nx.draw_networkx_edges(G,pos = positions, edgelist=edges, style='solid')
    
    plt.title(label=title,fontsize=20,color="black")
    # displays the graph without axis
    plt.axis('off')
    #saves image
    plt.savefig("part1.png", format="PNG")
    plt.show() 

create_corr_network_1(G,'Original')
In [17]:
def create_corr_network_2(G, corr_direction):

    ##Creates a copy of the graph
    H = G.copy()

    ##Checks all the edges and removes some based on corr_direction
    for stock1, stock2, weight in G.edges(data=True):
        ##if we only want to see the positive correlations we then delete the edges with weight smaller than 0
        if corr_direction == "positive":
            if weight["weight"] <0:
                H.remove_edge(stock1, stock2)
        ##this part runs if the corr_direction is negative and removes edges with weights equal or largen than 0
        else:
            if weight["weight"] >=0:
                H.remove_edge(stock1, stock2)
    return H
                
H1 = create_corr_network_2(G,corr_direction='positive'); 
H2 = create_corr_network_2(G,corr_direction='negative'); 

create_corr_network_1(H1,'positive') 
create_corr_network_1(H2,'negative')
In [18]:
#function to create and display networks from the correlatin matrix. 

def create_corr_network_3(G, corr_direction, min_correlation):
    ##Creates a copy of the graph
    H = G.copy()
    
    ##Checks all the edges and removes some based on corr_direction
    for stock1, stock2, weight in G.edges(data=True):
        ##if we only want to see the positive correlations we then delete the edges with weight smaller than 0        
        if corr_direction == "positive":
            ####it adds a minimum value for correlation. 
            ####If correlation weaker than the min, then it deletes the edge
            if weight["weight"] <0 or weight["weight"] < min_correlation:
                H.remove_edge(stock1, stock2)
        ##this part runs if the corr_direction is negative and removes edges with weights equal or largen than 0
        else:
            ####it adds a minimum value for correlation. 
            ####If correlation weaker than the min, then it deletes the edge
            if weight["weight"] >=0 or weight["weight"] > min_correlation:
                H.remove_edge(stock1, stock2)
                
    
    #crates a list for edges and for the weights
    edges,weights = zip(*nx.get_edge_attributes(H,'weight').items())
    
    ### increases the value of weights, so that they are more visible in the graph
    weights = tuple([(1+abs(x))**2 for x in weights])
    
    #####calculates the degree of each node
    
    d = nx.degree(H)
    #####creates list of nodes and a list their degrees that will be used later for their sizes
    nodelist, node_sizes = zip(*d)

    #positions
    positions=nx.circular_layout(H)
    
    #Figure size
    plt.figure(figsize=(10,10))

    #draws nodes
    nx.draw_networkx_nodes(H,positions,node_color='#DA70D6',nodelist=nodelist,
                           #####the node size will be now based on its degree
                           node_size=tuple([x**3 for x in node_sizes]),alpha=0.8)
    
    #Styling for labels
    nx.draw_networkx_labels(H, positions, font_size=8, font_family='sans-serif')
    
    ###edge colors based on weight direction
    if corr_direction == "positive":
        edge_colour = plt.cm.GnBu 
    else:
        edge_colour = plt.cm.PuRd
        
    #draws the edges
    nx.draw_networkx_edges(H, positions, edgelist=edges,style='solid',
                          ###adds width=weights and edge_color = weights 
                          ###so that edges are based on the weight parameter 
                          ###edge_cmap is for the color scale based on the weight
                          ### edge_vmin and edge_vmax assign the min and max weights for the width
                          width=weights, edge_color = weights, edge_cmap = edge_colour,
                          edge_vmin = min(weights), edge_vmax=max(weights))

    plt.title(label = corr_direction,fontsize=20,color="black")
    # displays the graph without axis
    plt.axis('off')
    #saves image
    plt.savefig("part5" + corr_direction + ".png", format="PNG")
    plt.show()
In [19]:
create_corr_network_3(G,corr_direction='positive',min_correlation = 0.35)
create_corr_network_3(G,corr_direction='negative',min_correlation = 0)
In [20]:
#part 3: model implementation
In [21]:
#choose stock market
#si.tickers_dow()
#si.tickers_nasdaq()
#lets try sp500
ticker_list2 = si.tickers_sp500();         
ticker_str2 = " ".join(ticker_list);
In [22]:
Multi_Stock_2 = yf.download(tickers=ticker_str2,period='5y',interval='1mo',rounding=True);

[*********************100%***********************]  5346 of 5346 completed

80 Failed downloads:
- HMACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ATAKR: Period '5y' is invalid, must be one of ['1d', '5d']
- NOVVR: Period '5y' is invalid, must be one of ['1d', '5d']
- PEARW: Period '5y' is invalid, must be one of ['1d', '5d']
- IGTAR: Period '5y' is invalid, must be one of ['1d', '5d']
- LBBBR: Period '5y' is invalid, must be one of ['1d', '5d']
- AQUNR: Period '5y' is invalid, must be one of ['1d', '5d']
- GBRGR: Period '5y' is invalid, must be one of ['1d', '5d']
- ARIZR: Period '5y' is invalid, must be one of ['1d', '5d']
- HSPOR: Period '5y' is invalid, must be one of ['1d', '5d']
- FXCOR: Period '5y' is invalid, must be one of ['1d', '5d']
- MCAFR: Period '5y' is invalid, must be one of ['1d', '5d']
- BSGAR: Period '5y' is invalid, must be one of ['1d', '5d']
- KYCHR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZXZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- ALSAR: Period '5y' is invalid, must be one of ['1d', '5d']
- IMAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- GLSTR: Period '5y' is invalid, must be one of ['1d', '5d']
- WTMAR: Period '5y' is invalid, must be one of ['1d', '5d']
- CSLMR: Period '5y' is invalid, must be one of ['1d', '5d']
- PFG: Error occurred while retrieving timeseries from Redis, keys: [RedisKey [key=PFG, cluster=finance]]
- QOMOR: Period '5y' is invalid, must be one of ['1d', '5d']
- MCACR: Period '5y' is invalid, must be one of ['1d', '5d']
- MSSAR: Period '5y' is invalid, must be one of ['1d', '5d']
- ATMVR: Period '5y' is invalid, must be one of ['1d', '5d']
- RFACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZBZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- ZAZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- AIBBR: Period '5y' is invalid, must be one of ['1d', '5d']
- MARXR: Period '5y' is invalid, must be one of ['1d', '5d']
- BMAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- BLACR: Period '5y' is invalid, must be one of ['1d', '5d']
- JGGCR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZXYZ.A: No data found, symbol may be delisted
- WINVR: Period '5y' is invalid, must be one of ['1d', '5d']
- GLLIR: Period '5y' is invalid, must be one of ['1d', '5d']
- GDSTR: Period '5y' is invalid, must be one of ['1d', '5d']
- PNACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZCZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- CLOER: Period '5y' is invalid, must be one of ['1d', '5d']
- KACLR: Period '5y' is invalid, must be one of ['1d', '5d']
- ACAXR: Period '5y' is invalid, must be one of ['1d', '5d']
- NVACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ADOCR: Period '5y' is invalid, must be one of ['1d', '5d']
- MBTCR: Period '5y' is invalid, must be one of ['1d', '5d']
- YOTAR: Period '5y' is invalid, must be one of ['1d', '5d']
- BREZR: Period '5y' is invalid, must be one of ['1d', '5d']
- EFHTR: Period '5y' is invalid, must be one of ['1d', '5d']
- BRACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZWZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- BWAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- SVIIR: Period '5y' is invalid, must be one of ['1d', '5d']
- MCAGR: Period '5y' is invalid, must be one of ['1d', '5d']
- GBBKR: Period '5y' is invalid, must be one of ['1d', '5d']
- RWODR: Period '5y' is invalid, must be one of ['1d', '5d']
- FLFVR: Period '5y' is invalid, must be one of ['1d', '5d']
- PPHPR: Period '5y' is invalid, must be one of ['1d', '5d']
- BLEUR: Period '5y' is invalid, must be one of ['1d', '5d']
- ATMCR: Period '5y' is invalid, must be one of ['1d', '5d']
- PLTNR: Period '5y' is invalid, must be one of ['1d', '5d']
- SAGAR: Period '5y' is invalid, must be one of ['1d', '5d']
- WHLRL: Period '5y' is invalid, must be one of ['1d', '5d']
- DMAQR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZJZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- CLRCR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZVZZC: Period '5y' is invalid, must be one of ['1d', '5d']
- JWACR: Period '5y' is invalid, must be one of ['1d', '5d']
- ZVZZT: Period '5y' is invalid, must be one of ['1d', '5d']
- ONFOW: Period '5y' is invalid, must be one of ['1d', '5d']
- ASCAR: Period '5y' is invalid, must be one of ['1d', '5d']
- FEXDR: Period '5y' is invalid, must be one of ['1d', '5d']
- EMCGR: Period '5y' is invalid, must be one of ['1d', '5d']
- BNIXR: Period '5y' is invalid, must be one of ['1d', '5d']
- BRLIR: Period '5y' is invalid, must be one of ['1d', '5d']
- ENERR: Period '5y' is invalid, must be one of ['1d', '5d']
- TENKR: Period '5y' is invalid, must be one of ['1d', '5d']
- STRCW: Period '5y' is invalid, must be one of ['1d', '5d']
- HHGCR: Period '5y' is invalid, must be one of ['1d', '5d']
- GFGDR: Period '5y' is invalid, must be one of ['1d', '5d']
- ASCBR: Period '5y' is invalid, must be one of ['1d', '5d']
In [23]:
#simple sieve
#get rid of unnecessary stocks(fluctuation is too small) by percentage(formula: (highest - lowest)/lowest within a month)
#not consecutive month but at least m months with a percentage larger than p in 5 years
#we set the percentage which is p to be 0.3 and at least m=25 months 
#return adjusted df_percentage and a list of unnecessary stocks
p=0.3; m=25;
df_percentage = (Multi_Stock_2['High']-Multi_Stock_2['Low'])/Multi_Stock_2['Low']; 
list_original = df_percentage.columns.values.tolist();
df_percentage = df_percentage.loc[:,(df_percentage >= p).sum() >= m];
df_percentage.dropna(axis = 0, how = 'all', inplace = True);#drop recent days which are NaN value at the bottom

list_adjusted = df_percentage.columns.values.tolist();
list_unnecessary = list(set(list_original) - set(list_adjusted));
In [24]:
df_percentage
Out[24]:
AACG AADI AAOI ABEO ABIO ABUS ABVC ACB ACER ACET ... Z ZG ZIVO ZKIN ZLAB ZTEK ZVRA ZYME ZYNE ZYXI
Date
2018-03-01 0.052941 0.111111 0.119736 0.178516 0.369565 0.136842 0.000000 0.192647 0.123272 0.479144 ... 0.077882 0.072919 0.100000 0.076159 0.098687 0.060000 0.095890 0.178606 0.124700 0.263889
2018-04-01 0.078895 0.479290 0.531500 0.598837 0.413043 0.244898 0.000000 0.368322 0.180000 0.375615 ... 0.176000 0.176572 0.375000 0.203993 0.281075 0.558824 0.632653 0.735776 0.446701 0.208791
2018-05-01 0.084000 0.270517 0.554485 0.379421 0.812500 0.216216 0.058201 0.235189 0.142286 0.530588 ... 0.224995 0.225954 0.857143 0.083086 0.433613 0.270270 0.416298 0.653819 0.284698 0.420000
2018-06-01 0.315920 0.500000 0.243191 0.199346 0.372549 0.301724 0.000000 0.298869 0.223796 0.471261 ... 0.151621 0.139324 0.727273 1.369085 0.135393 0.250000 0.265207 1.036517 0.332623 0.339552
2018-07-01 0.224138 0.110827 0.351424 0.305556 1.461538 0.650685 0.000000 0.462633 0.688943 1.511498 ... 0.166393 0.166937 17.000000 0.479365 0.201809 0.333333 0.826667 0.297942 0.726825 0.217021
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2022-11-01 0.211180 0.154412 0.415842 0.526154 0.183784 0.259740 0.215543 0.372881 0.270270 0.370301 ... 0.385821 0.369565 0.236641 0.321429 0.676962 0.190141 0.351981 0.245364 0.560000 0.259740
2022-12-01 0.646018 0.152542 0.442933 0.743590 0.217822 0.277273 0.274194 0.802680 2.590551 1.761905 ... 0.266238 0.282170 0.279167 1.286689 0.381341 0.451852 0.200980 0.611111 0.367347 0.111111
2023-01-01 0.213115 0.120833 0.393443 0.285141 0.244240 0.400000 2.116667 0.258824 0.521531 0.266846 ... 0.393771 0.395807 0.694444 2.350000 0.633115 0.135714 0.415730 0.477428 0.346154 0.252723
2023-02-01 0.657143 0.220547 0.296610 0.324201 0.154229 0.178988 0.273846 0.446579 0.174129 0.284750 ... 0.223214 0.215038 0.339286 0.492537 0.284271 0.294964 0.158397 0.259627 0.456522 0.207067
2023-03-01 0.383333 0.571429 0.265217 0.524887 0.129353 0.171103 0.379310 0.310976 2.184615 0.379019 ... 0.148440 0.148533 0.187500 0.426230 0.386530 0.295455 0.434568 0.204852 0.289744 0.404711

61 rows × 1247 columns

In [25]:
#simple sieve
#stocks have at least n consecutive months with percentage larger than 25%
n = 4; x = 0.25; 
df_consecutive = df_percentage.loc[:, (df_percentage >= x).rolling(n).sum().eq(n).any()];
df_consecutive = df_consecutive[df_percentage >= x]
df_consecutive
Out[25]:
AACG AADI AAOI ABEO ABIO ABUS ABVC ACB ACER ACET ... Z ZG ZIVO ZKIN ZLAB ZTEK ZVRA ZYME ZYNE ZYXI
Date
2018-03-01 NaN NaN NaN NaN 0.369565 NaN NaN NaN NaN 0.479144 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.263889
2018-04-01 NaN 0.479290 0.531500 0.598837 0.413043 NaN NaN 0.368322 NaN 0.375615 ... NaN NaN 0.375000 NaN 0.281075 0.558824 0.632653 0.735776 0.446701 NaN
2018-05-01 NaN 0.270517 0.554485 0.379421 0.812500 NaN NaN NaN NaN 0.530588 ... NaN NaN 0.857143 NaN 0.433613 0.270270 0.416298 0.653819 0.284698 0.420000
2018-06-01 0.315920 0.500000 NaN NaN 0.372549 0.301724 NaN 0.298869 NaN 0.471261 ... NaN NaN 0.727273 1.369085 NaN 0.250000 0.265207 1.036517 0.332623 0.339552
2018-07-01 NaN NaN 0.351424 0.305556 1.461538 0.650685 NaN 0.462633 0.688943 1.511498 ... NaN NaN 17.000000 0.479365 NaN 0.333333 0.826667 0.297942 0.726825 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2022-11-01 NaN NaN 0.415842 0.526154 NaN 0.259740 NaN 0.372881 0.270270 0.370301 ... 0.385821 0.369565 NaN 0.321429 0.676962 NaN 0.351981 NaN 0.560000 0.259740
2022-12-01 0.646018 NaN 0.442933 0.743590 NaN 0.277273 0.274194 0.802680 2.590551 1.761905 ... 0.266238 0.282170 0.279167 1.286689 0.381341 0.451852 NaN 0.611111 0.367347 NaN
2023-01-01 NaN NaN 0.393443 0.285141 NaN 0.400000 2.116667 0.258824 0.521531 0.266846 ... 0.393771 0.395807 0.694444 2.350000 0.633115 NaN 0.415730 0.477428 0.346154 0.252723
2023-02-01 0.657143 NaN 0.296610 0.324201 NaN NaN 0.273846 0.446579 NaN 0.284750 ... NaN NaN 0.339286 0.492537 0.284271 0.294964 NaN 0.259627 0.456522 NaN
2023-03-01 0.383333 0.571429 0.265217 0.524887 NaN NaN 0.379310 0.310976 2.184615 0.379019 ... NaN NaN NaN 0.426230 0.386530 0.295455 0.434568 NaN 0.289744 0.404711

61 rows × 1247 columns

In [48]:
#build a dict containing target stocks' valuation measure
dow_list = si.tickers_dow();
dow_stats = {};
for ticker in dow_list:
    temp = si.get_stats_valuation(ticker);
    temp = temp.iloc[:,:2];
    temp.columns = ["Attribute", "Recent"];
    dow_stats[ticker] = temp;
In [58]:
dow_stats['AAPL']
Out[58]:
Attribute Recent
0 Market Cap (intraday) 2.45T
1 Enterprise Value 2.51T
2 Trailing P/E 26.32
3 Forward P/E 26.25
4 PEG Ratio (5 yr expected) 2.62
5 Price/Sales (ttm) 6.48
6 Price/Book (mrq) 43.25
7 Enterprise Value/Revenue 6.48
8 Enterprise Value/EBITDA 19.63
In [50]:
#Price-to-Book Ratio of stocks in row stock market
combined_stats = pd.concat(dow_stats);
combined_stats = combined_stats.reset_index();
del combined_stats["level_1"]; #unnecessary column
# update column names
combined_stats.columns = ["Ticker", "Attribute", "Recent"];
pe_ratios = combined_stats[combined_stats["Attribute"]=="Price/Book (mrq)"].reset_index(); #change to another valuation
pe_ratios['Recent'] = pe_ratios['Recent'].astype(float); #important!!!! waste my 1.5 hours on this!!! without this step, the sort below does not work anyway!!!
pe_ratios_sorted = pe_ratios.sort_values('Recent',ascending=False);
pe_ratios_sorted;
In [51]:
#multi attributes and new ratio test
#Enterprise Value/Revenue and pb value
Attribute2 = combined_stats["Attribute"][combined_stats["Attribute"]=="Enterprise Value/Revenue"].reset_index(drop=True);
Recent2 = combined_stats["Recent"][combined_stats["Attribute"]=="Enterprise Value/Revenue"].reset_index(drop=True);
og = combined_stats[combined_stats["Attribute"]=="Price/Book (mrq)"].reset_index(drop=True);
multi_Attributes = og.assign(Attribute2 = Attribute2, Recent2 = Recent2);#change to another valuation
multi_Attributes['Recent'] = multi_Attributes['Recent'].astype(float); #important!!!! 
multi_Attributes['Recent2'] = multi_Attributes['Recent2'].astype(float); #important!!!! 
multi_sorted = multi_Attributes.sort_values(['Recent', 'Recent2'], ascending=[False,False]);
multi_sorted
Out[51]:
Ticker Attribute Recent Attribute2 Recent2
11 HD Price/Book (mrq) 187.39 Enterprise Value/Revenue 2.16
0 AAPL Price/Book (mrq) 43.25 Enterprise Value/Revenue 6.48
1 AMGN Price/Book (mrq) 33.52 Enterprise Value/Revenue 5.79
26 V Price/Book (mrq) 12.37 Enterprise Value/Revenue 15.28
22 NKE Price/Book (mrq) 12.22 Enterprise Value/Revenue 3.84
21 MSFT Price/Book (mrq) 11.36 Enterprise Value/Revenue 10.00
17 KO Price/Book (mrq) 10.88 Enterprise Value/Revenue 6.74
23 PG Price/Book (mrq) 7.73 Enterprise Value/Revenue 4.56
12 HON Price/Book (mrq) 7.39 Enterprise Value/Revenue 3.77
4 CAT Price/Book (mrq) 7.00 Enterprise Value/Revenue 2.37
15 JNJ Price/Book (mrq) 6.19 Enterprise Value/Revenue 5.18
20 MRK Price/Book (mrq) 5.75 Enterprise Value/Revenue 4.75
25 UNH Price/Book (mrq) 5.63 Enterprise Value/Revenue 1.45
13 IBM Price/Book (mrq) 5.11 Enterprise Value/Revenue 2.60
6 CSCO Price/Book (mrq) 5.03 Enterprise Value/Revenue 3.68
29 WMT Price/Book (mrq) 4.90 Enterprise Value/Revenue 0.70
2 AXP Price/Book (mrq) 4.71 Enterprise Value/Revenue NaN
19 MMM Price/Book (mrq) 3.85 Enterprise Value/Revenue 2.04
5 CRM Price/Book (mrq) 3.17 Enterprise Value/Revenue 5.95
7 CVX Price/Book (mrq) 1.82 Enterprise Value/Revenue 1.26
24 TRV Price/Book (mrq) 1.78 Enterprise Value/Revenue 1.21
8 DIS Price/Book (mrq) 1.77 Enterprise Value/Revenue 2.49
9 DOW Price/Book (mrq) 1.71 Enterprise Value/Revenue 0.85
27 VZ Price/Book (mrq) 1.70 Enterprise Value/Revenue 2.40
16 JPM Price/Book (mrq) 1.40 Enterprise Value/Revenue NaN
28 WBA Price/Book (mrq) 1.38 Enterprise Value/Revenue 0.45
14 INTC Price/Book (mrq) 1.22 Enterprise Value/Revenue 2.17
10 GS Price/Book (mrq) 0.96 Enterprise Value/Revenue NaN
18 MCD Price/Book (mrq) NaN Enterprise Value/Revenue 10.42
3 BA Price/Book (mrq) NaN Enterprise Value/Revenue 2.41
In [30]:
si.get_analysts_info('nflx')
Out[30]:
{'Earnings Estimate':   Earnings Estimate  Current Qtr. (Mar 2023)  Next Qtr. (Jun 2023)  \
 0   No. of Analysts                    30.00                 29.00   
 1     Avg. Estimate                     2.85                  3.06   
 2      Low Estimate                     2.70                  2.53   
 3     High Estimate                     3.16                  3.91   
 4      Year Ago EPS                     3.53                  3.20   
 
    Current Year (2023)  Next Year (2024)  
 0                34.00             33.00  
 1                11.43             14.48  
 2                 9.50             11.62  
 3                12.95             17.13  
 4                 9.95             11.43  ,
 'Revenue Estimate':           Revenue Estimate Current Qtr. (Mar 2023) Next Qtr. (Jun 2023)  \
 0          No. of Analysts                      28                   26   
 1            Avg. Estimate                   8.17B                 8.5B   
 2             Low Estimate                   8.12B                8.27B   
 3            High Estimate                    8.2B                8.89B   
 4           Year Ago Sales                   7.87B                7.97B   
 5  Sales Growth (year/est)                   3.90%                6.60%   
 
   Current Year (2023) Next Year (2024)  
 0                  32               32  
 1              34.52B           38.62B  
 2              33.62B           37.06B  
 3                 38B           42.44B  
 4              31.62B           34.52B  
 5               9.20%           11.90%  ,
 'Earnings History':   Earnings History 3/30/2022 6/29/2022 9/29/2022 12/30/2022
 0         EPS Est.      2.89      2.94      2.13       0.45
 1       EPS Actual      3.53       3.2       3.1       0.12
 2       Difference      0.64      0.26      0.97      -0.33
 3       Surprise %    22.10%     8.80%    45.50%    -73.30%,
 'EPS Trend':           EPS Trend  Current Qtr. (Mar 2023)  Next Qtr. (Jun 2023)  \
 0  Current Estimate                     2.85                  3.06   
 1        7 Days Ago                     2.85                  3.06   
 2       30 Days Ago                     2.93                  2.84   
 3       60 Days Ago                     2.96                  2.85   
 4       90 Days Ago                     2.99                  2.89   
 
    Current Year (2023)  Next Year (2024)  
 0                11.43             14.48  
 1                11.43             14.48  
 2                10.47             13.48  
 3                10.52             13.46  
 4                10.49             13.34  ,
 'EPS Revisions':        EPS Revisions  Current Qtr. (Mar 2023)  Next Qtr. (Jun 2023)  \
 0     Up Last 7 Days                      NaN                   NaN   
 1    Up Last 30 Days                     11.0                  19.0   
 2   Down Last 7 Days                      NaN                   NaN   
 3  Down Last 30 Days                      NaN                   NaN   
 
    Current Year (2023)  Next Year (2024)  
 0                  NaN               NaN  
 1                 30.0              22.0  
 2                  NaN               NaN  
 3                  NaN               NaN  ,
 'Growth Estimates':            Growth Estimates     NFLX  Industry  Sector(s)  S&P 500
 0              Current Qtr.  -19.30%       NaN        NaN      NaN
 1                 Next Qtr.   -4.40%       NaN        NaN      NaN
 2              Current Year   14.90%       NaN        NaN      NaN
 3                 Next Year   26.70%       NaN        NaN      NaN
 4  Next 5 Years (per annum)   15.97%       NaN        NaN      NaN
 5  Past 5 Years (per annum)   32.55%       NaN        NaN      NaN}
In [28]:
si.get_analysts_info('nflx')['Growth Estimates']
Out[28]:
Growth Estimates NFLX Industry Sector(s) S&P 500
0 Current Qtr. -19.30% NaN NaN NaN
1 Next Qtr. -4.40% NaN NaN NaN
2 Current Year 14.90% NaN NaN NaN
3 Next Year 26.70% NaN NaN NaN
4 Next 5 Years (per annum) 15.97% NaN NaN NaN
5 Past 5 Years (per annum) 32.55% NaN NaN NaN
In [69]:
#Candlestick Price Chart
In [76]:
#single stock
company = 'AAPL'
df_Candlestick1 = yf.Ticker(company).history(period='5y',interval='1d')
fig = go.Figure(data=[go.Candlestick(x=df_Candlestick1.index, open=df_Candlestick1['Open'], high=df_Candlestick1['High'], low=df_Candlestick1['Low'], close=df_Candlestick1['Close'])])

fig.update_layout(title = f'{company}: Candlestick Price Chart', xaxis_tickfont_size = 14)
fig.update_layout(xaxis_rangeslider_visible = False)
    
fig.show()
In [77]:
#multi stocks
#simplified 
companys = ['QQQ', 'MSFT']; # Tickers to be analysed
companys_str = " ".join(companys);

fig = make_subplots(rows=1, cols=1,shared_xaxes=True,vertical_spacing=0.02)

for i in companys:
    df_Candlestick1 = yf.Ticker(i).history(period='5y',interval='1d');
    fig.add_trace(go.Candlestick(x=df_Candlestick1.index,open=df_Candlestick1['Open'],high=df_Candlestick1['High'],low=df_Candlestick1['Low'],close=df_Candlestick1['Close'],name=i),row=1,col=1)

    

fig.data[1].increasing.fillcolor = 'ORANGE'
fig.data[1].increasing.line.color = 'ORANGE'
fig.data[1].decreasing.fillcolor = 'BLACK' #blank: 'rgba(0,0,0,0)'
fig.data[1].decreasing.line.color = 'BLACK'



fig.update_layout(title = f'{companys_str}: Candlestick Price Chart', xaxis_tickfont_size = 14)
fig.update_layout(xaxis_rangeslider_visible = False)
    
fig.show()
In [78]:
#multi stocks
#manual replication 
company = 'QQQ MSFT'
df_Candlestick1 = yf.Ticker('QQQ').history(period='5y',interval='1d');
df_Candlestick2 = yf.Ticker('MSFT').history(period='5y',interval='1d');
fig = make_subplots(rows=1, cols=1,shared_xaxes=True,vertical_spacing=0.02)

fig.add_trace(go.Candlestick(x=df_Candlestick1.index,
                open=df_Candlestick1['Open'],
                high=df_Candlestick1['High'],
                low=df_Candlestick1['Low'],
                close=df_Candlestick1['Close'],name='QQQ'),
               row=1,col=1)

fig.add_trace(go.Candlestick(x=df_Candlestick2.index,
                open=df_Candlestick2['Open'],
                high=df_Candlestick2['High'],
                low=df_Candlestick2['Low'],
                close=df_Candlestick2['Close'],name='MSFT'),
               row=1,col=1)
fig.data[1].increasing.fillcolor = 'ORANGE'
fig.data[1].increasing.line.color = 'ORANGE'
fig.data[1].decreasing.fillcolor = 'BLACK' #blank: 'rgba(0,0,0,0)'
fig.data[1].decreasing.line.color = 'BLACK'

fig.update_layout(title = f'{company}: Candlestick Price Chart', xaxis_tickfont_size = 14)
fig.update_layout(xaxis_rangeslider_visible = False)
    
fig.show()
In [111]:
#candlestick and trending lines(volume-weighted average price)
stock = 'AAPL';
df = yf.Ticker(stock).history(start="2022-01-15", end="2022-07-15",interval="1h");
fig = go.Figure(data=[go.Candlestick(x=df.index,open=df['Open'],high=df['High'],low=df['Low'],close=df['Close'],name=stock)])
v = df['Volume'].values
tp = (df['Low'] + df['Close'] + df['High']).div(3).values
df = df.assign(vwap=((tp * v).cumsum() / v.cumsum()))
fig = make_subplots(rows=2, cols=1,shared_xaxes=True,vertical_spacing=0.02)
fig.add_trace(go.Candlestick(x=df.index,open=df['Open'],high=df['High'],low=df['Low'],close=df['Close'],name=stock),row=1,col=1)

fig.add_trace(go.Scatter(x=df.index,y=df['vwap'],mode='lines',name='vwap',line=dict(color='royalblue',width=2)),row=2,col=1)
              
fig.update_layout(height=600, width=1000,title=f'{stock} & volume-weighted average price')
fig.show()
In [ ]:
In [ ]:
In [ ]:
In [37]:
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

N=14
df = pd.DataFrame({'Open': np.random.randint(1,29,N),
                   'Close': np.random.randint(1,29,N),
                   'Low': np.random.randint(1,29,N),
                   'High': np.random.randint(1,29,N),
                   'Action': np.random.choice(['sell', 'buy'],N),
                   'System Quality Number': np.random.randint(1,29,N)})


def chart_strategy(df):
    fig = make_subplots(rows=3, cols=1)

    fig.add_trace(go.Candlestick(x = df.index,
                                open = df['Open'],
                                close = df['Close'],
                                low = df['Low'],
                                high = df['High']),
                 row = 1, col = 1)

    fig.add_trace(go.Scatter(x = df.index, y = df['System Quality Number']), 
                             row = 2, col = 1)

    fig.add_trace(go.Scatter(x = df.index, y = df['Action']), row = 3, col =1)

    fig.update_xaxes(row=1, col=1, rangeslider_thickness=0.05)
    fig.update_layout(width=900, height=900)

    return fig

chart_strategy(df)
In [38]:
dff = Ticker('MSFT AAPL').history(period='5y',interval='1d')
dff
Out[38]:
open high low close volume adjclose dividends splits
symbol date
MSFT 2018-03-26 90.610001 94.000000 90.400002 93.779999 56396800.0 89.895889 0.0 0.0
2018-03-27 94.940002 95.139999 88.510002 89.470001 56569000.0 85.764412 0.0 0.0
2018-03-28 89.820000 91.230003 88.870003 89.389999 52501100.0 85.687714 0.0 0.0
2018-03-29 90.180000 92.290001 88.400002 91.269997 45867500.0 87.489861 0.0 0.0
2018-04-02 90.470001 90.879997 87.510002 88.519997 48515400.0 84.853752 0.0 0.0
... ... ... ... ... ... ... ... ... ...
AAPL 2023-03-16 152.160004 156.460007 151.639999 155.850006 76161100.0 155.850006 0.0 0.0
2023-03-17 156.080002 156.740005 154.279999 155.000000 98862500.0 155.000000 0.0 0.0
2023-03-20 155.070007 157.820007 154.149994 157.399994 73641400.0 157.399994 0.0 0.0
2023-03-21 157.320007 159.399994 156.539993 159.279999 73938300.0 159.279999 0.0 0.0
2023-03-22 159.300003 162.139999 157.809998 157.830002 69165700.0 157.830002 0.0 0.0

2514 rows × 8 columns

In [39]:
tickers = Ticker('aapl nflx', asynchronous=True)
df_company = tickers.history()
df_pivot = df_company.reset_index().pivot(index='date', columns='symbol', values='adjclose')
df_pivot;
fig, ax = plt.subplots(figsize=(10,6))
ax = sns.lineplot(data=df_pivot, palette=['r','b'], dashes=False)
In [40]:
import pandas as pd
from yahooquery import Ticker

tickers = Ticker('aapl nflx', asynchronous=True)
df_company = tickers.history()

df_pivot = df_company.reset_index().pivot(index='date', columns='symbol', values='adjclose')
import matplotlib.pyplot as plt
import matplotlib.dates as md
import matplotlib.ticker as mtick

import seaborn as sns
sns.set_theme()

fig, ax = plt.subplots(figsize=(10,6))

ax = sns.lineplot(data=df_pivot, palette=['r','b'], dashes=False)

# adjust axes for readability
ax.xaxis.set_major_locator(md.WeekdayLocator(byweekday = 1))
ax.set_xlim(df_pivot.index.min(), df_pivot.index.max())
ax.yaxis.set_major_locator(mtick.MultipleLocator(50))

plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
In [ ]:
In [41]:
df_pivot_perc = df_pivot.div(df_pivot.iloc[0,:]).mul(100)
fig, ax = plt.subplots(figsize=(10,6))

ax = sns.lineplot(data=df_pivot_perc, palette=['r','b'], dashes=False)
ax.xaxis.set_major_locator(md.WeekdayLocator(byweekday = 1))
ax.set_xlim(df_pivot.index.min(), df_pivot.index.max())

fmt = '%.0f%%'
yticks = mtick.FormatStrFormatter(fmt)
ax.yaxis.set_major_formatter(yticks)
ax.yaxis.set_major_locator(mtick.MultipleLocator(10))

plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
In [ ]:
In [ ]:
In [ ]:
In [43]:
dff
Out[43]:
open high low close volume adjclose dividends
symbol date
MSFT 2018-03-22 91.269997 91.750000 89.660004 89.790001 38604700 84.741531 0.0
2018-03-23 89.500000 90.459999 87.080002 87.180000 44068900 82.278275 0.0
2018-03-26 90.610001 94.000000 90.400002 93.779999 56396800 88.507210 0.0
2018-03-27 94.940002 95.139999 88.510002 89.470001 56569000 84.439514 0.0
2018-03-28 89.820000 91.230003 88.870003 89.389999 52501100 84.364021 0.0
... ... ... ... ... ... ... ...
2023-03-15 259.980011 266.480011 259.209991 265.440002 46028000 265.440002 0.0
2023-03-16 265.209991 276.559998 263.279999 276.200012 54768800 276.200012 0.0
2023-03-17 278.260010 283.329987 276.320007 279.429993 69492200 279.429993 0.0
2023-03-20 276.980011 277.480011 269.850006 272.230011 43466600 272.230011 0.0
2023-03-21 16:00:04-04:00 274.880005 274.950012 269.529999 273.779999 32485178 273.779999 0.0

1258 rows × 7 columns

In [ ]:
#https://subscription.packtpub.com/book/data/9781789618518/4/ch04lvl1sec26/implementing-the-fama-french-three-factor-model-in-python
In [81]:
from pandas_datareader.famafrench import get_available_datasets
import pandas_datareader.data as web
In [91]:
ff_dict = web.DataReader('F-F_Research_Data_Factors', 'famafrench', 
                         start='2014-01-01')
In [92]:
ff_dict
Out[92]:
{0:          Mkt-RF   SMB   HML    RF
 Date                             
 2014-01   -3.32  0.90 -2.07  0.00
 2014-02    4.65  0.37 -0.31  0.00
 2014-03    0.43 -1.85  4.93  0.00
 2014-04   -0.19 -4.20  1.17  0.00
 2014-05    2.06 -1.88 -0.13  0.00
 ...         ...   ...   ...   ...
 2022-09   -9.35 -0.79  0.06  0.19
 2022-10    7.83  0.09  8.05  0.23
 2022-11    4.60 -3.40  1.38  0.29
 2022-12   -6.41 -0.68  1.32  0.33
 2023-01    6.65  5.03 -4.08  0.35
 
 [109 rows x 4 columns],
 1:       Mkt-RF    SMB    HML    RF
 Date                            
 2014   11.71  -7.86  -1.45  0.02
 2015    0.09  -3.99  -9.65  0.02
 2016   13.30   6.66  22.75  0.20
 2017   21.51  -4.97 -13.51  0.80
 2018   -6.95  -3.21  -9.73  1.83
 2019   28.28  -6.11 -10.34  2.15
 2020   23.66  13.18 -46.56  0.45
 2021   23.56  -3.89  25.53  0.04
 2022  -21.60  -6.82  25.80  1.43,
 'DESCR': 'F-F Research Data Factors\n-------------------------\n\nThis file was created by CMPT_ME_BEME_RETS using the 202301 CRSP database. The 1-month TBill return is from Ibbotson and Associates, Inc. Copyright 2023 Kenneth R. French\n\n  0 : (109 rows x 4 cols)\n  1 : Annual Factors: January-December (9 rows x 4 cols)'}
In [93]:
ff_dict.keys()
Out[93]:
dict_keys([0, 1, 'DESCR'])
In [94]:
print(ff_dict['DESCR'])

F-F Research Data Factors
-------------------------

This file was created by CMPT_ME_BEME_RETS using the 202301 CRSP database. The 1-month TBill return is from Ibbotson and Associates, Inc. Copyright 2023 Kenneth R. French

  0 : (109 rows x 4 cols)
  1 : Annual Factors: January-December (9 rows x 4 cols)
In [96]:
ff_dict[0].head()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_28516\3261296504.py in <module>
----> 1 ff_dict[0:].head()

TypeError: unhashable type: 'slice'
In [ ]:
In [ ]:
In [ ]:
In [ ]:
#fama-french-multi-factor-models
#https://www.quantconnect.com/learning/articles/introduction-to-financial-python/fama-french-multi-factor-models
In [24]:
#https://yahooquery.dpguthrie.com/guide/ticker/financials/
#https://finance.yahoo.com/quote/AAPL/balance-sheet/
Market Price per Share = Current market price of the share
Book Value per Share = (Total assets - intangible assets - total liabilities) ÷ number of outstanding shares
Market Price per Share/Book Value per Share

  File "C:\Users\zijun\AppData\Local\Temp\ipykernel_28516\756052767.py", line 1
    https://yahooquery.dpguthrie.com/guide/ticker/financials/
          ^
SyntaxError: invalid syntax
In [ ]:
#the lower a company's price-to-book ratio is, the higher price to sales ratio, the higher pe ratio and the lower PEG ratio
In [94]:
aapl = Ticker('aacg')
aapl.quotes;
In [35]:
aapl = Ticker('aapl')
aapl.valuation_measures
Out[35]:
'Valuation data unavailable for aapl'
In [28]:
aapl = Ticker('aapl')
types = ['TotalDebt','GoodwillAndOtherIntangibleAssets']
A=aapl.get_financial_data('GoodwillAndOtherIntangibleAssets',frequency='q', trailing=False)
A
Out[28]:
'Cash Flow data unavailable for aapl'
In [ ]:
In [ ]:
In [ ]:
#machine learning
#https://www.youtube.com/watch?v=AXBhrLongC8
In [ ]:
In [ ]:
In [79]:
A=(Multi_Stock_2['High']-Multi_Stock_2['Low'])/Multi_Stock_2['Low'];
B=df_percentage.loc[:,(df_percentage >= x).sum() >= 20]
C=B.columns.values.tolist()
D = df_percentage.columns.values.tolist()
list(set(D) - set(C))
Out[79]:
['OTRK', 'CEMI', 'BNGO', 'SLS', 'STAF']
In [56]:
OTRK=yf.download(tickers='ZIONP',period='5y',interval='1d',rounding=True)

[*********************100%***********************]  1 of 1 completed
In [57]:
((OTRK['High']-OTRK['Low'])>1).sum()
Out[57]:
60
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [35]:
idx = pd.date_range("2018-01-01", periods=10, freq="M")

df_2 = pd.DataFrame(
    {
        'A':[0, 0.4, 0.5, 0.3, 0,0,0,0,0,0],
        'B':[0, 0.6, 0.8,0, 0.3, 0.3, 0.9, 0.7,0,0],
        'C':[0,0,0.5, 0.4, 0.4, 0.2,0,0,0,0],
        'D':[0.4,0, 0.6, 0.5, 0.7, 0.2,0, 0.9, 0.8,0],
        'E':[0.4, 0.3, 0.2, 0.7, 0.7, 0.8,0,0,0,0],
        'F':[0,0,0.6, 0.7,0.8, 0.3, 0.4, 0.1,0,0]
    },
    index=idx
)



df_2
Out[35]:
A B C D E F
2018-01-31 0.0 0.0 0.0 0.4 0.4 0.0
2018-02-28 0.4 0.6 0.0 0.0 0.3 0.0
2018-03-31 0.5 0.8 0.5 0.6 0.2 0.6
2018-04-30 0.3 0.0 0.4 0.5 0.7 0.7
2018-05-31 0.0 0.3 0.4 0.7 0.7 0.8
2018-06-30 0.0 0.3 0.2 0.2 0.8 0.3
2018-07-31 0.0 0.9 0.0 0.0 0.0 0.4
2018-08-31 0.0 0.7 0.0 0.9 0.0 0.1
2018-09-30 0.0 0.0 0.0 0.8 0.0 0.0
2018-10-31 0.0 0.0 0.0 0.0 0.0 0.0
In [37]:
df_2.loc[:, (df_2 >= 0.5).rolling(3).sum().eq(3).any()]
Out[37]:
D E F
2018-01-31 0.4 0.4 0.0
2018-02-28 0.0 0.3 0.0
2018-03-31 0.6 0.2 0.6
2018-04-30 0.5 0.7 0.7
2018-05-31 0.7 0.7 0.8
2018-06-30 0.2 0.8 0.3
2018-07-31 0.0 0.0 0.4
2018-08-31 0.9 0.0 0.1
2018-09-30 0.8 0.0 0.0
2018-10-31 0.0 0.0 0.0
In [55]:
df_consecutive = pd.DataFrame();
df_consecutive.insert(0, '0' , df_2.iloc[:,0], True);
df_column = df_2.columns.values.tolist();
for i in range(len(df_column)):
    for k, v in df_2[:,i][df_2.iloc[:,i] > 0.3].groupby((df_2.iloc[:,i] <= 0.3)):
        df_consecutive.insert(i+1, df_column[i] , v, True)
df_consecutive.drop(df_consecutive.columns[0], axis=1)
df_consecutive

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3628             try:
-> 3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

TypeError: '(slice(None, None, None), 0)' is an invalid key

During handling of the above exception, another exception occurred:

InvalidIndexError                         Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_7264\3365135746.py in <module>
      3 df_column = df_2.columns.values.tolist();
      4 for i in range(len(df_column)):
----> 5     for k, v in df_2[:,i][df_2.iloc[:,i] > 0.3].groupby((df_2.iloc[:,i] <= 0.3)):
      6         df_consecutive.insert(i+1, df_column[i] , v, True)
      7 df_consecutive.drop(df_consecutive.columns[0], axis=1)

~\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   3503             if self.columns.nlevels > 1:
   3504                 return self._getitem_multilevel(key)
-> 3505             indexer = self.columns.get_loc(key)
   3506             if is_integer(indexer):
   3507                 indexer = [indexer]

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3634                 #  InvalidIndexError. Otherwise we fall through and re-raise
   3635                 #  the TypeError.
-> 3636                 self._check_indexing_error(key)
   3637                 raise
   3638 

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in _check_indexing_error(self, key)
   5649             # if key is not a scalar, directly raise an error (the code below
   5650             # would convert to numpy arrays and raise later any way) - GH29926
-> 5651             raise InvalidIndexError(key)
   5652 
   5653     @cache_readonly

InvalidIndexError: (slice(None, None, None), 0)
In [46]:
df_consecutive = df_2.iloc[:,0]
df_consecutive

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3628             try:
-> 3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 0

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_7264\1281873712.py in <module>
----> 1 df_consecutive = df_2.loc[:,0]
      2 df_consecutive

~\anaconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
    959             if self._is_scalar_access(key):
    960                 return self.obj._get_value(*key, takeable=self._takeable)
--> 961             return self._getitem_tuple(key)
    962         else:
    963             # we by definition only have the 0th axis

~\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
   1138         with suppress(IndexingError):
   1139             tup = self._expand_ellipsis(tup)
-> 1140             return self._getitem_lowerdim(tup)
   1141 
   1142         # no multi-index, so validate all of the indexers

~\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_lowerdim(self, tup)
    865                 # We don't need to check for tuples here because those are
    866                 #  caught by the _is_nested_tuple_indexer check above.
--> 867                 section = self._getitem_axis(key, axis=i)
    868 
    869                 # We should never have a scalar section here, because

~\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
   1203         # fall thru to straight lookup
   1204         self._validate_key(key, axis)
-> 1205         return self._get_label(key, axis=axis)
   1206 
   1207     def _get_slice_axis(self, slice_obj: slice, axis: int):

~\anaconda3\lib\site-packages\pandas\core\indexing.py in _get_label(self, label, axis)
   1151     def _get_label(self, label, axis: int):
   1152         # GH#5667 this will fail if the label is not present in the axis.
-> 1153         return self.obj.xs(label, axis=axis)
   1154 
   1155     def _handle_lowerdim_multi_index_axis0(self, tup: tuple):

~\anaconda3\lib\site-packages\pandas\core\generic.py in xs(self, key, axis, level, drop_level)
   3847         if axis == 1:
   3848             if drop_level:
-> 3849                 return self[key]
   3850             index = self.columns
   3851         else:

~\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   3503             if self.columns.nlevels > 1:
   3504                 return self._getitem_multilevel(key)
-> 3505             indexer = self.columns.get_loc(key)
   3506             if is_integer(indexer):
   3507                 indexer = [indexer]

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:
-> 3631                 raise KeyError(key) from err
   3632             except TypeError:
   3633                 # If we have a listlike key, _check_indexing_error will raise

KeyError: 0
In [57]:
df_2.iloc[:,0]
consecutive = lambda x: df_2.iloc[:,x][df_2.iloc[:,x] > 0.3].groupby((df_2.iloc[:,x] <= 0.3))
map(consecutive,[1, 2, 3])
Out[57]:
<map at 0x1c2a8647f40>
In [35]:
for k, v in (lambda x: df_2.iloc[:,x][df_2.iloc[:,x] > 0.3].groupby((df_2.iloc[:,x] <= 0.3).cumsum()))([1,2]):
    print(v)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_7264\873683607.py in <module>
----> 1 for k, v in (lambda x: df_2.iloc[:,x][df_2.iloc[:,x] > 0.3].groupby((df_2.iloc[:,x] <= 0.3).cumsum()))([1,2]):
      2     print(v)

~\AppData\Local\Temp\ipykernel_7264\873683607.py in <lambda>(x)
----> 1 for k, v in (lambda x: df_2.iloc[:,x][df_2.iloc[:,x] > 0.3].groupby((df_2.iloc[:,x] <= 0.3).cumsum()))([1,2]):
      2     print(v)

~\anaconda3\lib\site-packages\pandas\core\frame.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)
   7719         # error: Argument "squeeze" to "DataFrameGroupBy" has incompatible type
   7720         # "Union[bool, NoDefault]"; expected "bool"
-> 7721         return DataFrameGroupBy(
   7722             obj=self,
   7723             keys=by,

~\anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)
    880             from pandas.core.groupby.grouper import get_grouper
    881 
--> 882             grouper, exclusions, obj = get_grouper(
    883                 obj,
    884                 keys,

~\anaconda3\lib\site-packages\pandas\core\groupby\grouper.py in get_grouper(obj, key, axis, level, sort, observed, mutated, validate, dropna)
    891         # allow us to passing the actual Grouping as the gpr
    892         ping = (
--> 893             Grouping(
    894                 group_axis,
    895                 gpr,

~\anaconda3\lib\site-packages\pandas\core\groupby\grouper.py in __init__(self, index, grouper, obj, level, sort, observed, in_axis, dropna)
    541             if getattr(self.grouping_vector, "ndim", 1) != 1:
    542                 t = self.name or str(type(self.grouping_vector))
--> 543                 raise ValueError(f"Grouper for '{t}' not 1-dimensional")
    544 
    545             self.grouping_vector = index.map(self.grouping_vector)

ValueError: Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional
In [162]:
df[df['value'] == 0].groupby((df['value'] != 0).cumsum())

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3628             try:
-> 3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'value'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_15544\4172185969.py in <module>
----> 1 df[df['value'] == 0].groupby((df['value'] != 0).cumsum())

~\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   3503             if self.columns.nlevels > 1:
   3504                 return self._getitem_multilevel(key)
-> 3505             indexer = self.columns.get_loc(key)
   3506             if is_integer(indexer):
   3507                 indexer = [indexer]

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:
-> 3631                 raise KeyError(key) from err
   3632             except TypeError:
   3633                 # If we have a listlike key, _check_indexing_error will raise

KeyError: 'value'
In [ ]:
#consecutive months
#range >7%
df_consecutive = pd.DataFrame();
df_consecutive.insert(0, '0' , df_percentage.iloc[:,0], True);
df_column = df_percentage.columns.values.tolist();
for i in range(len(df_column)):
    for k, v in df_percentage.iloc[:,i][df_percentage.iloc[:,i] > 0.6].groupby((df_percentage.iloc[:,i] <= 0.6).cumsum()):
        df_consecutive.insert(i+1, df_column[i] , v, True)
df_consecutive.drop(df_consecutive.columns[0], axis=1)
df_consecutive
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
#cointegration
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [113]:
#log return
Each_Stock['Log returns'] = np.log(Each_Stock['Close']/data['Close'].shift());
#target company financial data

#not working

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_8420\462711037.py in <module>
      1 #log return
----> 2 Each_Stock['Log returns'] = np.log(Each_Stock['Close']/data['Close'].shift());
      3 #target company financial data
      4 aapl=yf.Ticker('aapl')
      5 aapl.get_financials()

NameError: name 'Each_Stock' is not defined
In [114]:
aapl=yf.Ticker('aapl')
aapl.get_financials()

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_8420\1602078515.py in <module>
      1 aapl=yf.Ticker('aapl')
----> 2 aapl.get_financials()

~\anaconda3\lib\site-packages\yfinance\base.py in get_financials(self, proxy, as_dict, pretty, freq, legacy)
   1593 
   1594     def get_financials(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
-> 1595         return self.get_income_stmt(proxy, as_dict, pretty, freq, legacy)
   1596 
   1597     def get_balance_sheet(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):

~\anaconda3\lib\site-packages\yfinance\base.py in get_income_stmt(self, proxy, as_dict, pretty, freq, legacy)
   1580             data = self._fundamentals.financials.get_income_scrape(freq=freq, proxy=proxy)
   1581         else:
-> 1582             data = self._fundamentals.financials.get_income_time_series(freq=freq, proxy=proxy)
   1583 
   1584         if pretty:

~\anaconda3\lib\site-packages\yfinance\scrapers\fundamentals.py in get_income_time_series(self, freq, proxy)
    111         res = self._income_time_series
    112         if freq not in res:
--> 113             res[freq] = self._fetch_time_series("income", freq, proxy=None)
    114         return res[freq]
    115 

~\anaconda3\lib\site-packages\yfinance\scrapers\fundamentals.py in _fetch_time_series(self, name, timescale, proxy)
    140 
    141         try:
--> 142             statement = self._create_financials_table(name, timescale, proxy)
    143 
    144             if statement is not None:

~\anaconda3\lib\site-packages\yfinance\scrapers\fundamentals.py in _create_financials_table(self, name, timescale, proxy)
    153             name = "financials"
    154 
--> 155         keys = self._get_datastore_keys(name, proxy)
    156         try:
    157             return self.get_financials_time_series(timescale, keys, proxy)

~\anaconda3\lib\site-packages\yfinance\scrapers\fundamentals.py in _get_datastore_keys(self, sub_page, proxy)
    160 
    161     def _get_datastore_keys(self, sub_page, proxy) -> list:
--> 162         data_stores = self._data.get_json_data_stores(sub_page, proxy)
    163 
    164         # Step 1: get the keys:

~\anaconda3\lib\site-packages\yfinance\data.py in wrapped(*args, **kwargs)
     39         args = tuple([tuple(arg) if isinstance(arg, list) else arg for arg in args])
     40         kwargs = {k: tuple(v) if isinstance(v, list) else v for k, v in kwargs.items()}
---> 41         return func(*args, **kwargs)
     42 
     43     # copy over the lru_cache extra methods to this wrapper to be able to access them

~\anaconda3\lib\site-packages\yfinance\data.py in get_json_data_stores(self, sub_page, proxy)
    309 
    310         # Decrypt!
--> 311         stores = decrypt_cryptojs_aes_stores(data, keys)
    312         if stores is None:
    313             # Maybe Yahoo returned old format, not encrypted

~\anaconda3\lib\site-packages\yfinance\data.py in decrypt_cryptojs_aes_stores(data, keys)
    160                 pass
    161         if not success:
--> 162             raise Exception("yfinance failed to decrypt Yahoo data response")
    163 
    164     decoded_stores = json.loads(plaintext)

Exception: yfinance failed to decrypt Yahoo data response
In [ ]:
### increases the value of weights, so that they are more visible in the graph
weights = tuple([(1+abs(x))**2 for x in weights])

###edge colors based on weight direction
if corr_direction == "positive":
    edge_colour = plt.cm.GnBu 
else:
    edge_colour = plt.cm.PuRd

#draws the edges
nx.draw_networkx_edges(H, positions, edge_list=edges,style='solid',
                      ###adds width=weights and edge_color = weights 
                      ###so that edges are based on the weight parameter 
                      ###edge_cmap is for the color scale based on the weight
                      ### edge_vmin and edge_vmax assign the min and max weights for the width
                      width=weights, edge_color = weights, edge_cmap = edge_colour,
                      edge_vmin = min(weights), edge_vmax=max(weights))
In [ ]:
def create_corr_network(G, corr_direction, min_correlation):

    ...

##Checks all the edges and removes some based on corr_direction
for stock1, stock2, weight in G.edges_iter(data=True):
    ##if we only want to see the positive correlations we then delete the edges with weight smaller than 0        
    if corr_direction == "positive":
        ####it adds a minimum value for correlation. 
        ####If correlation weaker than the min, then it deletes the edge
        if weight["weight"] <0 or weight["weight"] < min_correlation:
            H.remove_edge(stock1, stock2)
    ##this part runs if the corr_direction is negative and removes edges with weights equal or largen than 0
    else:
        ####it adds a minimum value for correlation. 
        ####If correlation weaker than the min, then it deletes the edge
        if weight["weight"] >=0 or weight["weight"] > min_correlation:
            H.remove_edge(stock1, stock2)
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#####calculates the degree of each node
d = nx.degree(H)
#####creates list of nodes and a list their degrees that will be used later for their sizes
nodelist, node_sizes = zip(*d.items())

#draws nodes
nx.draw_networkx_nodes(H,positions,node_color='#DA70D6',nodelist=nodelist,
                       #####the node size will be now based on its degree
                       node_size=tuple([x**3 for x in node_sizes]),alpha=0.8)
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